The present invention relates to medical imaging and, more particularly, to systems and methods for controlling radiation doses delivered when performing imaging processes using ionizing radiation.
In a computed tomography system, an x-ray source projects a beam which is collimated to lie within an X-Y plane of a Cartesian coordinate system, termed the “imaging plane.” The x-ray beam passes through the object being imaged, such as a medical patient or other non-medical patient or object, such as in industrial CT imaging, and impinges upon an array of radiation detectors. The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object and each detector produces a separate electrical signal that is a measurement of the beam attenuation. The attenuation measurements from all the detectors are acquired separately to produce the transmission profile at a particular view angle.
The source and detector array in a conventional CT system are rotated on a gantry within the imaging plane and around the object so that the angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements from the detector array at a given angle is referred to as a “view”, and a “scan” of the object comprises a set of views acquired at different angular orientations during one revolution of the x-ray source and detector. In a 2D scan, data is processed to construct an image that corresponds to a two dimensional slice taken through the object. The prevailing method for reconstructing an image from 2D data is referred to in the art as the filtered backprojection technique, however, other image reconstruction processes are also well known. This process converts the attenuation measurements from a scan into integers called “CT numbers” or “Hounsfield units”, which are used to control the brightness of a corresponding pixel on a display.
The drastically increased use of CT in modern clinical settings has generated serious public health concerns regarding the cancer risks associated with the radiation exposure from CT.
The current guiding principle in CT clinical practice is to use radiation dose levels as low as reasonably achievable while maintaining acceptable diagnostic accuracy. However, lowering radiation dose alone generally produces a noisier image and may seriously degrade diagnostic performance. Many algorithms have been proposed for controlling noise in CT, and these can be broadly categorized into 3 major types: projection space, image space, and iterative reconstruction.
Projection space techniques, which work on either the raw projection data or the log-transformed sinogram, attempt to reduce noise in the projection data domain prior to image reconstruction. In general, these techniques have the advantage that noise properties in projection space are fairly well understood. One potential drawback of projection-based methods is that they may result in some loss of image sharpness due to the fact that edges in projection data are not well-defined.
Image-space denoising involves applying linear or non-linear filters directly to the reconstructed images. Most such techniques (e.g. bilateral filtering, total variation denoising, non-local means denoising, and k-SVD denoising) take advantage of the strong structural and statistical properties of objects in image space (e.g. sharp edges, similarities between neighboring pixels). In CT, they can be implemented directly and without access to the raw data. However, CT noise in image space is difficult to model accurately and has strong spatial variations and correlations. It can therefore be more difficult for such techniques to achieve an optimal tradeoff between denoising and blurring or artifacts, or to get consistent performance across an entire scan volume.
Iterative reconstruction (IR) techniques are more accurately considered reconstruction rather than denoising techniques, and take advantage of statistical assumptions about the noise properties in projection space and structure in image space. IR techniques require access to the raw data and accurate knowledge of the details of the scanner geometry, photon statistics, data-acquisition and correction physics, thus highly dependent on specific scanner models. True IR is very computationally intensive (e.g., several hours per data set), which has prevented fast clinical application to date, although software methods and hardware methods have been investigated to accelerate the iterative procedure.
Due to the extremely high computational load of true IR, hybrid techniques have recently been developed that attempt to gain many of the benefits of true IR with much lower computational load (e.g. Sinogram AFirmed Iterative REconstruction (SAFIRE) from Siemens). Some of these are now available commercially, but whether they can achieve similar level of image quality improvement as true IR desire more physics and clinical evidence.
Despite the development of various IR methods, each is only available on some of the latest scanner models from each manufacturer. A large percentage of scanners currently in use in hospitals all over the world do not have these options. Our invention addresses the need for a denoising strategy that can be broadly used across the CT community, over a heterogeneous scanner fleet that encompasses different manufacturers as well as different models of varying age and software revision. These requirements lead us to consider image space denoising techniques, which are relatively simple to implement, work on the image data alone, and can be applied retrospectively. As mentioned above, it is difficult for image space techniques to model CT noise or scanner details accurately, and thus they may appear to necessarily be at a disadvantage with respect to projection space or IR methods. However, the spatial structure models in modern image denoising algorithms are significantly more advanced than the spatial regularization terms that are incorporated in current IR methods. It is thus not at all clear that image space results will necessarily be inferior.
Non-local means (NLM) denoising is an effective image denoising strategy that exploits the inherent redundant information present in most images. NLM generalizes the notion of finite spatial differences and utilizes a measure of difference between nearby image patches to estimate underlying image structure. This allows NLM to preserve a high degree of image texture and fine detail. However, the standard NLM algorithm uses a uniform filtering strength to denoise the image, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT images using a global filtering strength cannot achieve optimal denoising performance.